Benchmarking The Turkish Apparel Retail Industry Through Data Envelopment Analysis (Dea) And Data Visualization

7/30/2019 Benchmarking The Turkish Apparel Retail Industry Through Data Envelopment Analysis (Dea) And Data Visualization

1/14

1

Ertek, G., Can, M. A., and Ulus, F. (2007). "Benchmarking the Turkish apparel retail industry

through data envelopment analysis (DEA) and data visualization. EUROMA 2007, Ankara,Turkey.

Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as

above. You can download this final draft from http://research.sabanciuniv.edu.

BENCHMARKING THE TURKISH APPARELRETAIL INDUSTRY THROUGH DATA

ENVELOPMENT ANALYSIS (DEA) AND

DATA VISUALIZATION

Grdal Ertek, Merve Aye Can, Firdevs Ulus

Faculty of Engineering and Natural Sciences

Sabanci University, Orhanli, Tuzla, 34956, Istanbul, Turkey


2/14

2

ABSTRACT

This paper presents a benchmarking study of the Turkish apparel retailing

industry. We have applied the Data Envelopment Analysis (DEA) methodology to

determine the efficiencies of the companies in the industry. In the DEA model the

number of stores, number of corners, total sales area and number of employees

were included as inputs and annual sales revenue was included as the output. The

efficiency scores obtained through DEA were visualized for gaining insights about

the industry and revealing guidelines that can aid in strategic decision making.

Keywords: Data Envelopment Analysis (DEA), apparel retailing, industrial

benchmarking

INTRODUCTION

Retailing consists of the sale of goods/merchandise for personal or household consumption

either from a fixed location or from a fixed location and related subordinated services.

(Retailing, 2006). In order to survive in todays highly competitive environment, companies

should find ways of continuously improving themselves and should regularly benchmark

themselves against their competitors to assess their standing and to revise their competitive

strategies if necessary.

In this paper, an existing benchmarking methodology is applied to the Turkish apparel

retailing industry. The used methodology employs data visualization to interpret the results of

Data Envelopment Analysis (DEA), and has been applied earlier by Ulus et al. (2006) to

benchmark the logistics companies whose stocks are traded in the New York Stock Exchange

(NYSE). DEA is a nonparametric mathematical technique that is frequently preferred against

other analytical methods since it requires few assumptions about the units and magnitudes of

input data (Weill, 2004). This technique uses data regarding the inputs and outputs of the


3/14

3

entities in a group, and represents the efficiencies of these entities as a single computed value

ranging from 0 to 1. The entities are referred to as Decision Making Units (DMUs) and the values that denote efficiencies are referred to as the efficiency scores . The primary motivation of

our research is to prove that this methodology can be highly useful for benchmarking in the

retailing industry. In our study, the DMUs that are selected are the Turkish apparel retailers.

The efficiency scores of the DMUs are visualized in commercial software products, enabling us

to gain some crucial insights about this industry.

METHODOLOGIES

Data Envelopment Analysis (DEA)

Data Envelopment Analysis (DEA) is a technique that can be employed in the measurement of

the efficiencies of a set of Decision Making Units (DMUs) by using multiple inputs and outputs.

There are different possible cases when using the technique, such as single input & output case,

two inputs & one output case, etc. In the cases of multiple inputs and multiple outputs, the

weights have to be determined. The weights can be classified into two main groups, which are

fixed weights and variable weights. In the fixed weights approach, the same weights are used for

the input and outputs of all the DMUs and, in the variable weights approach, the best weights

are selected as a part of the DEA. DEA methodology varies also with respect to orientation,

being input oriented or output oriented . In the case of input oriented DEA, one explores how the

inputs can be reduced while still obtaining the same output levels. In the case of output oriented

model, one explores how the outputs can be increased with given fixed levels of inputs. The

inputs and outputs themselves are chosen according to the nature and the focus of the research.DMUs which have smaller values and larger outputs are preferable. The data used for the DMUs

can be integer, rational or real as long as they are nonnegative. The efficient frontier is formed

by the efficient DMUs that have efficiency score 1, and the efficiency scores of the other

inefficient DMUs are calculated accordingly (Cooper, 2006). In our study we decided to apply

the BCC Input Oriented Model.


4/14

4

Data Visualization

The visualization of the data is a crucial part of the analysis methodology, since it enables the

analyst to view the patterns and gain fundamental insights. With the emergence of information

visualization, it is now possible to employ new styles of visualizations in the data analysis

process. Information visualization is the growing field of computer science that combines data

mining, computer graphics and explanatory data analysis in pursuit of visually understanding

data (Keim 2002, Spence 2001). In our paper, input and output data, the efficiency scores and

other relevant data are visualized through colored scatter plots in Miner3D software (Miner3D)

and tile visualizations in Omniscope software (Visokio).

DATA COLLECTION

The data used in our study was obtained mainly from the August 2006 issue of the Turkishtime

magazine (Turkishtime) and was extended throughout the project by acquiring data from other

sources. The data in the Turkishtime magazine included the following fields for the retail

companies: number of stores, number of corners, sales areas (m), number of employees and

the annual sales revenue. The gathering of the missing data and extending the data with new

fields were the most challenging parts of the project and required a number of contacts with

companies through telephone and e-mail. The data regarding the number of visitors was not

used in the DEA study, since these values were missing for too many companies. Meanwhile, we

assumed that the number of corners for some (mostly small) companies (such as BARCIN) were

zero. Finally, data for 39 retailers was included in the DEA. The data for the 39 retailers was

compiled in a Microsoft Excel spreadsheet, and the correctness and the consistency of the data was checked. We have used the DEA-Solver software to compute the efficiency scores for the

selected DMUs. This software was developed by Kaoru Tone and comes with the DEA book by

Cooper et al. (2006). The data that is categorized as input and output are represented as headers

and the prefix (i) is inserted at the beginning of the input column headers and the prefix (o) is

inserted at the beginning of the output column headers. In applying the DEA, we selected the

inputs as the number of stores, number of corners, total sales area (m) and number of

employees. The only output of our model was selected to be the total annual sales revenue for


5/14

5

each retailer. The analysis was carried out through enabling macros to be run in Microsoft Excel

and by selecting the BCC input oriented model of DEA within the DEA-Solver software. Afterrunning the DEA-Solver, we obtained the efficiency scores of the DMUs (companies). We

analyzed these efficiency scores together with relevant data for the companies, such as the years

in which the companies were founded and the product categories that the companies sell.

ANALYSIS

The provided efficiency scores were combined with the retail data for visualizations in theMiner3D software. 12 out of the 39 companies were observed to have the efficiency score of

1.The data was visualized in scatter plots and colored scatter plots which enabled us to discover

hidden patterns and to come up with several insights. Our analysis of the visualization are

explained in the following section.

Efficiency vs. Foundation Year

The data related to the year of foundation of each company was collected in order to detect any

relations between the efficiency scores and the foundation years. The x axis is plotted as the

foundation year and the y axis is plotted as efficiency score (30 represents 1930 and 100

represents 2000 on the x axis).

A close examination of figure 1 provides an important insight: efficiency scores of the

companies do not display a visible increase or decrease based on the year of foundation. Thus,

for example one cannot state by looking at the plot that older, well-established companies are

more efficient compared to younger ones. For example, KANZ, which was founded in 1949, is

observed to have the efficiency score of 0.32, whereas DESIGN, which was founded in 2004, has

the perfect efficiency score of 1. However, it is observed that the efficiency scores exhibit much

larger variation for companies established in more recent years. One other pattern in figure 1 is

that out of companies founded before 1970 (with x values less than 70) only YKM and BARCIN


6/14

6

have efficiencies greater than 0.5. The remaining 6 companies founded before 1970 have very

low efficiencies.

Figure 1 - Efficiency vs. Foundation Year

Employee per Area vs. Area per Store

We computed the employee per area values and plotted them against area per store valuesas

shown in figure 2.

KANZ


7/14

7

In figure 2, values on the x axis shows area per store, values on the y axis shows employee per

area, and color shows the efficiency score of each company. Darker colors denote higherefficiency scores, and black denotes efficiency of 1. The five efficient companies (with black

color) at the lower part of the figure suggest a linear pattern within the interval (400, 750) m

per shop. Corresponding to any area per store value within this interval, one can find a

corresponding value for employee per m within the interval (0.01, 0.03). For example, a

company with 400 m area per store should keep in mind that there exists a company with

approximately 0.01 employees per m, corresponding to 4 employees per store. So this can be

considered as a lower bound on the number of employees that the company should employ,

since even the efficient companies in this industry employ 4 employees per store.

Figure 2 - Employee per Area vs. Area per Store


8/14

8

Efficiency vs. No of Stores

We have used Miner3D software to also explore and reveal patterns between number of stores,

store areas (summed over all stores) and the efficiency scores. In figure 3, values on the x axis

show the number of stores of each company, values on the y axis show the efficiency scores, and

sizes show total store areas of the companies.

Figure 3 indicates that a significant portion of the companies that perform well have large

total area, such as BOYNER, YKM, LC WAIK and KOTON. Yet, there also exist efficient

companies with small area, such as YESIL and DESA. One can observe that the number of stores

does not have a differentiating impact on efficiency, but when area exceeds a particular level,

efficiency is almost always observed. One company that draws attention is TWEEN. This

company has many stores and a significant area, but is very inefficient.


9/14

9

Figure 3 - Area vs. Number of Stores

Product Types and the Efficiency Scores

In our next analysis, we have grouped the companies according to the types of products they sell,

so that we can investigate whether it could be a good strategy for a company to focus on a

particular type of product. The product types are grouped and labeled as A (the companies that

sell more than 3 product types), C (classical wear), H (shoes and accessories), K (kids wear), S

(sport wear), and U (underwear). We plot the effiency scores against product types in figure 4.


10/14

10

Figure 4 shows that all four companies in company group A, namely BOYNER, YKM, KOTON

and DESIGN, are efficient. So it can be concluded that retailing a wide variety of apparelproduct types is viable strategy. Increasing the product range and serving customers in larger

centrally located stores (as the four companies mentioned above do) could be winning strategies.

All but one of the companies in the classical wear (C) group have low efficiency scores, almost

uniformly dispersed within the interval (0.1, 0.63). The obvious leader in this group is GERMIR,

which is the only efficient firm within group C. Both of the two companies that sell only kids

apparel (in the group K) have efficiency scores below 0.4, indicating that restricting the product

range to only kids apparel is not a good strategy. Meanwhile, it is observed that the retailers

which focus on underwear (within the group U) perform better with respect to the ones that

focus on kids apparel.

One striking insight of the figure is that there does not exist even a single company that has an

efficiency score within the range (0.75, 0.9). All the companies in the dataset have efficiency

scores of exactly 1 or scores below ~0.7.


11/14

11

Figure 4 Efficiency vs. Product Ranges

The relation between the efficiency scores and the product range was also examined by usingthe Omniscope software: In figure 5, size denotes number of stores and color denotes efficiency

score. . Darker tones of green indicate that the companies have higher efficiency scores. The

company groups are as in figure 4, labeled with letters of C, S, A, H, K and U.

As we had observed in figure 4, we again observe that the companies in group C exhibit very

low efficiency scores as a whole, whereas the companies in group A all have the efficiency score

of 1. In figure 5, we also observe that there exist too many companies in the classical wear (C)


12/14

12

group with a high number of stores. Thus, one possible explanation for the inefficiency of

companies in this group is the fierce competition among such a large number of companies. Theunderwear group (U) characteristics opposite to the classical wear group (C): There are only two

companies and very few stores.

Figure 5 - Efficiency vs. Product Types


13/14

13

Efficiency and Gender

Usage of the Omniscope software enabled us to also detect some important findings related to

the selection of target markets in terms of gender. We classified the companies into three groups

according to the gender of the targeted consumers, as serving only women (W), only men (M),

only kids (K), both women and men (WM) and women, men and kids altogether (WMK).

In figure 6, sizes of the tiles denote the number of stores, and colors denote efficiency. The

companies that serve only men (M), only women (W) or only kids (K) are inefficient in general,

but the companies that serve both genders, such as WM (both women and men) and WMK

(women, men and kids altogether) perform significantly better. These results suggest that

serving both genders, and even kids, is more beneficial for companies in this industry compared

to serving a single gender.

Figure 6 Efficiency and Gender


14/14

14

CONCLUSION

Benchmarking of Turkish apparel industry is carried out by using the DEA in this study and data

visualization. Our study presents many patterns that have not been observed before, and arms

managers in this subsector of the retail industry with actionable insights. We thus conclude that

the methodology that we employ is appropriate for benchmarking the companies in the selected

industry, and can be tested for benchmarking companies in other retailing sectors and in retail

industries of other countries.

REFERENCES

Cooper, W. W., Seiford, L. M., and Tone, K. (2006), Introduction to Data Envelopment Analysis

and its Uses , Springer, New York.

Keim, D. A. (2002), Information visualization and data mining, IEEE Transactions on

Visualization and Computer Graphics , Vol.8, No.1, pp. 1-8.

Miner3D. http://www.miner3d.com

Retailing. (2006, December 27). In Wikipedia, The Free Encylopedia. Retrieved 23:42,

December 28, 2006, from

http://en.wikipedia.org/w/index.php?title=Retailing&oldid=9672425

Spence, R. (2001), Information Visualization , ACM Press, Essex, England.

Ulus, F., Kose, O, Ertek, G. and Sen, S. (2006), "Financial benchmarking of transportationcompanies in the New York Stock Exchange (NYSE) through data envelopment analysis

(DEA) and visualization". 4th International Logistics and Supply Chain Congress, Izmir,

Turkey. Available under http://www.ertek.info

Weill, L. (2004), Measuring cost efficiency in European banking: a comparison of frontier

techniques, Journal of Productivity Analysis , Vol.21, No.2, pp. 133-152.

Visokio. http://www.visokio.com

Benchmarking The Turkish Apparel Retail Industry Through Data Envelopment Analysis (Dea) And Data Visualization

Documents